Einstein Prediction Builder: Thinking Through Predictions with Bias in Mind

Machine learning is an incredibly powerful tool that can be used for anything from suggesting an ice cream flavor to predicting customer churn. But, as much as we’d like it to be, it is not magic. Applying machine learning to a business problem involves finding patterns in your historical data and using those patterns to make predictions about new data. This means that if your data includes patterns that you don’t want to replicate going forward, or if the data you provide only represents a fraction of your use cases, your predictions will likely be inaccurate and unfair. As admins, we know how much easier and more efficient a user’s experience becomes when these patterns and predictions work correctly. So, let’s explore how these unwanted patterns can arise in your models and unintentionally lead to biased machine learning tools. We’ll also focus on how to think about your use cases